IP Library › Granted Patent US 12,430,766
Granted Patent B2
US 12,430,766 · App. 18/353,184 · Granted Sep 30, 2025

Methods and systems for analyzing brain lesions with longitudinal 3D MRI data

Inventor: Darin T Okuda (Coppell, TX)
Assignee: The Board of Regents of the University of Texas System
G06T7/0016A61B5/0042A61B5/055A61B5/7267A61B2576/026G06T2207/10088G06T2207/30016G06T2207/30096
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,430,766
App. No.
18/353,184
Granted
Sep 30, 2025
Kind
B2
Abstract

Some methods of analyzing one or more brain lesions of a patient comprise, for each of the lesion(s), calculating one or more lesion characteristics from a first 3-dimensional (3D) representation of the lesion obtained from data taken at a first time and a second 3D representation of the lesion obtained from data taken at a second time that is after the first time. The characteristic(s) can include a change, form the first time to the second time, in the lesion's volume and/or surface area, the lesion's displacement from the first time to the second time, and/or the lesion's theoretical radius ratio at each of the first and second times. Some methods comprise characterizing whether the patient has multiple sclerosis and/or the progression of multiple sclerosis in the patient based at least in part on the calculation of the lesion characteristic(s) of each of the lesion(s).

Claims (44)

1. A method of analyzing one or more lesions of a brain of a patient, the method comprising:

for each of the lesion(s), from a first 3-dimensional (3D) representation of the lesion obtained from data taken at a first time and a second 3D representation of the lesion obtained from data taken at a second time that is after the first time, calculating one or more lesion characteristics that include:

a change, from the first time to the second time, in the volume of the lesion;

a change, from the first time to the second time, in the surface area of the lesion;

a displacement of the lesion from the first time to the second time; and/or

the theoretical radius ratio of the lesion at each of the first and second times;

for each of the lesion(s), assessing whether one or more criteria are satisfied, the one or more criteria including:

a displacement-based criterion that is satisfied when the displacement is greater than or equal to a threshold displacement that is not determined based on the age of the patient; and

characterizing whether the patient has multiple sclerosis and/or the progression of multiple sclerosis in the patient based at least in part on the calculation of the lesion characteristic(s) of each of the lesion(s) and on the assessment of the one or more criteria for each of the lesion(s).

2. The method of claim 1 , comprising:

determining for at least a majority of the lesion(s) that at least one of the one or more criteria is satisfied; and

determining that the patient has multiple sclerosis.

3. The method of claim 1 , wherein the data taken at the first time and the data taken at the second time each is a 3D magnetic resonance imaging (MRI) image of the brain of the patient.

4. The method of claim 1 , wherein the data taken at the first time and the data taken at the second time each is a 3D magnetic resonance imaging (MRI) image of the brain of the patient.

5. The method of claim 1 , wherein the time elapsed between the first and second times is between 6 months and 4 years.

6. A system for analyzing one or more lesions of a brain of a patient, the system comprising one or more processors configured to:

for each of the lesion(s), from a first 3-dimensional (3D) representation of the lesion obtained from data taken at a first time and a second 3D representation of the lesion obtained from data taken at a second time that is after the first time, calculate one or more lesion characteristics that include:

a change, from the first time to the second time, in the volume of the lesion;

a change, from the first time to the second time, in the surface area of the lesion;

a displacement of the lesion from the first time to the second time; and/or

the theoretical radius ratio of the lesion at each of the first and second times;

for each of the lesion(s), assess whether one or more criteria are satisfied, the one or more criteria including:

a displacement-based criterion that is satisfied when the change in the position of the lesion is greater than or equal to a threshold displacement not based on the age of the patient; and

characterize whether the patient has multiple sclerosis and/or the progression of multiple sclerosis in the patient based at least in part on the calculation of the lesion characteristic(s) of each of the lesion(s) and on the assessment of the one or more criteria for each of the lesion(s).

7. The system of claim 6 , wherein the processor(s) are configured to:

for each of the lesion(s), assess whether one or more criteria are satisfied, the one or more criteria including:

a deformation-based criterion that is satisfied when the theoretical radius ratio of the lesion at each of the first and second times is greater than or equal to a threshold theoretical radius ratio; and

characterize whether the patient has multiple sclerosis and/or the progression of multiple sclerosis in the patient based at least in part on the assessment of the one or more criteria for each of the lesion(s).

8. The system of claim 7 , wherein:

the processor(s) are configured to assess whether the deformation-based criterion is satisfied without determining the threshold theoretical radius ratio based on the age of the patient.

9. The system of claim 6 , wherein the data taken at the first time and the data taken at the second time each is a 3D magnetic resonance imaging (MRI) image of the brain of the patient.

10. A method of analyzing one or more lesions of a brain of a patient, the method comprising:

for each of the lesion(s), from a first 3-dimensional (3D) representation of the lesion obtained from data taken at a first time and a second 3D representation of the lesion obtained from data taken at a second time that is after the first time, calculating one or more lesion characteristics that include:

a change, from the first time to the second time, in the volume of the lesion;

a change, from the first time to the second time, in the surface area of the lesion;

a displacement of the lesion from the first time to the second time; and/or

the theoretical radius ratio of the lesion at each of the first and second times;

for each of the lesion(s), assessing whether one or more criteria are satisfied, the one or more criteria including:

a deformation-based criterion that is satisfied when the theoretical radius ratio of the lesion at each of the first and second times is greater than or equal to a threshold theoretical radius ratio that is not determined based on the age of the patient; and

characterizing whether the patient has multiple sclerosis and/or the progression of multiple sclerosis in the patient based at least in part on the calculation of the lesion characteristic(s) of each of the lesion(s) and on the assessment of the one or more criteria for each of the lesion(s).

11. The method of claim 10 , comprising:

determining for at least a majority of the lesion(s) that at least one of the one or more criteria is satisfied; and

determining that the patient has multiple sclerosis.

12. The method of claim 10 , wherein the data taken at the first time and the data taken at the second time each is a 3D magnetic resonance imaging (MRI) image of the brain of the patient.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 17, 2023
From: OKUDA, DARIN T.
To: THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM
Reel/Frame 065246/0956 →
Continuity (3)
Continuation 17031010 · Sep 24, 2020
Provisional Application 62905079 · Sep 24, 2019
Related Publication 20240005506A1 · Jan 4, 2024
References Cited (55)
US 10743837B2 · Sandhu et al. · 2020 [cited by applicant]
US 11093787B2 · Okuda et al. · 2021 [cited by applicant]
US 20040236217A1 · Cerwin et al. · 2004 [cited by applicant]
US 20110255761A1 · O'Dell · 2011 [cited by examiner]
US 20140270451A1 · Zach · 2014 [cited by examiner]
US 20150080703A1 · Reiman · 2015 [cited by examiner]
US 20150119697A1 · Penn · 2015 [cited by applicant]
US 20160109539A1 · Mardor · 2016 [cited by examiner]
US 20170256056A1 · Jain · 2017 [cited by examiner]
US 20180127507A1 · Greenberg et al. · 2018 [cited by applicant]
US 20190197347A1 · Okuda et al. · 2019 [cited by applicant]
US 20190248885A1 · Liddelow et al. · 2019 [cited by applicant]
US 20190370970A1 · Kim · 2019 [cited by examiner]
US 20200005461A1 · Yip · 2020 [cited by examiner]
US 20200147065A1 · Moraitis · 2020 [cited by examiner]
US 20210041518A1 · Okuda et al. · 2021 [cited by applicant]
US 20210374459A1 · Okuda et al. · 2021 [cited by applicant]
WO WO2018005939 · 2018 [cited by applicant]
WO WO2019165464 · 2019 [cited by applicant]
Absinta et al., “Association of Chronic Active Multiple Sclerosis Lesions With Disability In Vivo.” [cited by applicant]
Absinta et al., “Identification of Chronic Active Multiple Sclerosis Lesions on 3T MRI.” AJNR Am [cited by applicant]
Brown et al., “Do Cerebral Small Vessel Disease and Multiple Sclerosis Share Common Mechanisms of White Matter Injury?” [cited by applicant]
Cannistraro et al., “CNS small vessel disease: A clinical review.” [cited by applicant]
Dal-Bianco et al., “Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 T magnetic resonance imaging.” [cited by applicant]
Dufouil et al., “Longitudinal study of blood pressure and white matter hyperintensities: the EVA MRI Cohort.” [cited by applicant]
Elliott et al., “Slowly expanding/evolving lesions as a magnetic resonance imaging marker of chronic active multiple sclerosis lesions.” [cited by applicant]
Extended European Search Report issued in Corresponding European Application No. 20868659.2, dated Sep. 7, 2023. [cited by applicant]
Fernando et al., “White matter lesions in an unselected cohort of the elderly: molecular pathology suggests origin from chronic hypoperfusion injury.” [cited by applicant]
Frischer et al., “Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque.” [cited by applicant]
Gouw et al., “Progression of white matter hyperintensities and incidence of new lacunes over a 3-year period: the Leukoaraiosis and Disability study.” [cited by applicant]
Hammond et al. “Quantitative in vivo magnetic resonance imaging of multiple sclerosis at 7 Tesla with sensitivity to iron.” [cited by applicant]
Hansen et al. “Post-gadolinium 3-dimensional spatial, surface, and structural characteristics of glioblastomas differentiate pseudoprogression from true tumor progression.” [cited by applicant]
Hoogeveen et al., “MRI evaluation of the relationship between carotid artery endothelial shear stress and brain white matter lesions in migraine.” [cited by applicant]
International Preliminary Report on Patentability issued in corresponding International Application No. PCT/US2020/052452 mailed Dec. 17, 2020. [cited by applicant]
International Search Report issued in corresponding International Application No. PCT/US2020/052452 mailed Dec. 17, 2020. [cited by applicant]
Köhler et al., “Exploring individual multiple sclerosis lesion volume change over time: Development of an algorithm for the analyses of longitudinal quantitative MRI measures” [cited by applicant]
Lassmann et al., “Remyelination in multiple sclerosis.” [cited by applicant]
Ma et al. “Lesion registration for longitudinal disease tracking in an imaging informatics-based multiple sclerosis eFolder” [cited by applicant]
Neuropathology Group. Medical Research Council Cognitive F, Aging S. Pathological correlates of late-onset dementia in a multicentre, community-based population in England and Wales. Neuropathology Group of the Medical … [cited by applicant]
Newton et al. “Three-Dimensional Shape and Surface Features Distinguish Multiple Sclerosis Lesions from Nonspecific White Matter Disease.” [cited by applicant]
Okuda et al. “Incidental MRI anomalies suggestive of multiple sclerosis: the radiologically isolated syndrome.” [cited by applicant]
Okuda et al., “Utility of shape evolution and displacement in the classification of chronic multiple sclerosis lesions” [cited by applicant]
Sachdev et al., “Progression of white matter hyperintensities in elderly individuals over 3 years.” [cited by applicant]
Sati et al. “The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative.” [cited by applicant]
Schmidt et al., “Austrian Stroke Prevention S. Progression of cerebral white matter lesions: 6-year results of the Austrian Stroke Prevention Study.” [cited by applicant]
Sethi et al., “Slowly eroding lesions in multiple sclerosis”. [cited by applicant]
Sivakolundu et al., “Three-Dimensional Lesion Phenotyping and Physiologic Characterization Inform Remyelination Ability in Multiple Sclerosis.” [cited by applicant]
Solomon et al., “Misdiagnosis of multiple sclerosis: Impact of the 2017 McDonald criteria on clinical practice.” [cited by applicant]
Solomon et al., “The contemporary spectrum of multiple sclerosis misdiagnosis: A multicenter study.” [cited by applicant]
Tallantyre et al. “Ultra-high-field imaging distinguishes MS lesions from asymptomatic white matter lesions.” [cited by applicant]
Thompson et al., “Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria.” [cited by applicant]
Van Veluw et al., “Different microvascular alterations underlie microbleeds and microinfarcts.” [cited by applicant]
Wang et al., “Classification of multiple sclerosis and non-specific white matter lesions using spherical harmonics descriptors.” [cited by applicant]
Wardlaw et al., “Small vessel disease: mechanisms and clinical implications.” [cited by applicant]
Ziemssen et al., “Optimizing treatment success in multiple sclerosis.” [cited by applicant]